Study on Preferential Tax System Concerning Overseas Mineral Resources Exploration
Bibliographic record
Abstract
Although the gross of mineral resource is abundant inChina,the individual possessing is lower;there are many lean ore;and the mineral resources are in short supply;the waste of resourcesand environment pollution are common.However,mineral resourcesin a certain extent determine the ability of sustainable developmentin our country.In this situation,this paper proposes that we shouldfully utilize both domestic and international markets and resources,encourage mining enterprises to go global for mineral resourcesexploration,establish overseas mineral resource reserve base and production base.This paper also points out that overseas mineralresources exploration preferential tax policy has the necessity andfeasibility for encouraging mineral enterprises conduct mineralresource exploration abroad.We should refer to the preferential taxpolicies and achievements of American,Japan,Canada and othercountries in the overseas mineral resources mineral exploration,make several overseas mineral resources exploration preferentialtax policies which apply to China's national conditions and conformto international practices,do our best to inspire enterprises inour country to overseas exploration and development of mineralresources via effective tax preferential policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".